1. Field of the Invention
The present invetion relates to a pattern recognition device which enables high accuracy recognition of input patterns through effective learning of reference pattern vectors.
2. Description of the Prior Art
Recent advancement in pattern recognition techniques, such as the character recognition, speech recognition, and drawing reading, is remarkable and includes the development of various pattern recognition devices like voice activated work processor (dictation machine) and handwritten character reading and inputting devices. In a pattern recognition device of this kind, the recognition for the input pattern is obtained generally by carrying out the processing of matching between a reference pattern vector which is prepared in advance and the input pattern. For this reason, in order to make high accuracy recognition for the input patterns, it becomes necessary to prepare a highly substantial reference pattern vector through learning of the reference pattern vector by collecting a large number of patterns that are used in practice and input patterns when the pattern recognition device is used. However, the fact that it is necessary to collect a large number of patterns in advance and that it requires enormous processing time for the learning processing of the reference pattern vector that utilizes the collected patterns, presents a major task to be accomplished. For instance, when learning is carried out by the K-L expansion of the syllable recognition carried out by the K-L expansion of the syllable reference pattern vector that consists of 101 categories represented as 256-dimensional vectors, for speech recognition of syllables, it requires about 5 hours of processing time even when used with a large high-speed computer with operation processing speed of 1 MIPS.
Now, conventionally, the pattern collection and the learning of the reference pattern vector are carried out generally in a different state from that for the recognition of input patterns. Namely, it is ordinarily done by switching the operational mode from the recognition condition to the learning condition. Thus, for example, in the on-line handwritten character reader, the user executes the learning of the reference pattern vector by interrupting the pattern recognition processing and by inputting the pattern for learning. For this reason, there was an inconvenience that the recognition processing for an input pattern cannot be executed during the learning processing of the reference pattern vector.
In addition, when recognition is made for the patterns of intermittently inputted human-spoken words, like in the voice activated word processor (dictation machine), the processing section for the sum of products operation for the recognition processing of the input pattern is active over only a certain fixed duration in which the patterns are inputted in the recognition condition. For the remaining times, in spite of its being in the recognition condition, it actually remains in the waiting condition for the patterns to be inputted, so that the arithmetic processing unit is in a paused state in effect. Therefore, time losses are produced and the arithmetic units of the recognition device operate at a lower efficiency.
On the other hand, to achieve recognition with high accuracy of the patterns inputted by an unspecified speaker or an unspecified writer, it is conceivable to collect the input patterns to be used for the recognition processing as the patterns for learning, and to carry out the learning of the reference pattern vector by these patterns. By so doing, it becomes possible to carry out an effective collection of the patterns for learning the reference pattern vector, as well as to achieve a substantiation of the reference pattern vector in an easy manner. However, in order to learn the numerous patterns that are collected in this manner by setting the learning condition, there will be many difficulties, for example, it requires an even larger amount of processing time.